{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How To: Accessing Raw Simulation Results\n",
    "\n",
    "As we saw in the model checking section, you may obtain the raw simulation results rather than the plot. To activate this feature, use the ``return_df`` flag and set it to ``True``.\n",
    "The following cell obtains these and stores the values in a variable \"df\". This way you are able to work with the data outside ``BPTK_Py``!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from BPTK_Py.bptk import bptk\n",
    "bptk = bptk()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = bptk.plot_scenarios(\n",
    "    scenario_managers=[\"smSimpleProjectManagement\"],\n",
    "    scenarios=[\"scenario120\"], \n",
    "    equations=[\"openTasks\"],\n",
    "    title=\"Deadline changes\\n\",\n",
    "    x_label=\"Time\",start_date=\"1/1/2018\",freq=\"M\",\n",
    "    y_label=\"Marketing Budget (USD)\",\n",
    "    kind=\"line\",\n",
    "    return_df=True ## <--- HERE\n",
    "    ,series_names = {\"smSimpleProjectManagement_scenario120_openTasks\" : \"openTasks\"}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may want to learn a little about the data. The following code calls the ``head()`` and ``describe()`` functions of the dataFrame. Head return the first 5 elements and Describe gives some important information on the data.\n",
    "For instance, we learn that there are 121 elements in the dataFrame (\"count\"). This is correct as the model simulates from t=0 to t=120. Further values are the mean, standard deviation, min, max and the 25th / 50th and 75th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************\n",
      "Properties of the dataFrame\n",
      "\t first 5 elements:\n",
      "             openTasks\n",
      "2018-01-31  120.000000\n",
      "2018-02-28  118.904800\n",
      "2018-03-31  117.809096\n",
      "2018-04-30  116.712881\n",
      "2018-05-31  115.616145\n",
      "\n",
      "Main description of the dataFrame\n",
      "        openTasks\n",
      "count  121.000000\n",
      "mean    53.582193\n",
      "std     38.468884\n",
      "min      0.000000\n",
      "25%     18.411872\n",
      "50%     53.185085\n",
      "75%     86.886076\n",
      "max    120.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"***************************\")\n",
    "print(\"Properties of the dataFrame\")\n",
    "print(\"\\t first 5 elements:\")\n",
    "print(df.head())\n",
    "print(\"\")\n",
    "print(\"Main description of the dataFrame\")\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To select only certain periods, you may use two different approaches. \n",
    "1. Go by t and use the list index representation\n",
    "2. Go by date (if you created a time series using ``start_date``)\n",
    "\n",
    "In both cases, you need to supply square brackets and the selected range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BY INDEX\n",
      "             openTasks\n",
      "2018-01-31  120.000000\n",
      "2018-02-28  118.904800\n",
      "2018-03-31  117.809096\n",
      "2018-04-30  116.712881\n",
      "2018-05-31  115.616145\n",
      "2018-06-30  114.518882\n",
      "\n",
      "BY YEAR-MONTH:\n",
      "             openTasks\n",
      "2018-01-31  120.000000\n",
      "2018-02-28  118.904800\n",
      "2018-03-31  117.809096\n",
      "2018-04-30  116.712881\n",
      "2018-05-31  115.616145\n",
      "2018-06-30  114.518882\n",
      "\n",
      "CHECK FOR EQUALITY OF BOTH\n",
      "            openTasks\n",
      "2018-01-31       True\n",
      "2018-02-28       True\n",
      "2018-03-31       True\n",
      "2018-04-30       True\n",
      "2018-05-31       True\n",
      "2018-06-30       True\n"
     ]
    }
   ],
   "source": [
    "# Select 06 first months\n",
    "by_index = df[0:6] \n",
    "\n",
    "# Select all values of the months January to June 2018:\n",
    "by_year = df[\"2018-01\":\"2018-06\"]\n",
    "\n",
    "print(\"BY INDEX\")\n",
    "print(by_index)\n",
    "\n",
    "print(\"\")\n",
    "print(\"BY YEAR-MONTH:\")\n",
    "print(by_year)\n",
    "\n",
    "print(\"\")\n",
    "print(\"CHECK FOR EQUALITY OF BOTH\")\n",
    "print(by_index == by_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you even saw how easy it was to check for value equality, just issue ``by_index == by_year``. The return type is a ``Series`` again and you may use it for further computations if assigned to a variable.\n",
    "Let us now simulate the equation \"closedTasks\" and generate a second dataFrame for and append it to the existing dataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_closed = bptk.plot_scenarios(\n",
    "    scenario_managers=[\"smSimpleProjectManagement\"],\n",
    "    scenarios=[\"scenario120\"], \n",
    "    equations=[\"closedTasks\"],\n",
    "    title=\"Deadline changes\\n\",\n",
    "    x_label=\"Time\",start_date=\"1/1/2018\",freq=\"M\",\n",
    "    y_label=\"Tasks\",\n",
    "    kind=\"line\",\n",
    "    return_df=True,\n",
    "    series_names= {\"smSimpleProjectManagement_scenario120_closedTasks\":\"closedTasks\"}\n",
    "    )\n",
    "\n",
    "\n",
    "df[\"closedTasks\"] = df_closed[\"closedTasks\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see how easy it was to add the series \"closedTasks\" of ``df_closed`` to ``df``. Similarly, we may create new series using computation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-31    120.0\n",
      "2018-02-28    120.0\n",
      "2018-03-31    120.0\n",
      "2018-04-30    120.0\n",
      "2018-05-31    120.0\n",
      "Freq: M, Name: initialOpenTasks, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df[\"initialOpenTasks\"] = df[\"openTasks\"] + df[\"closedTasks\"]\n",
    "print(df[\"initialOpenTasks\"].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, the DataFrame makes sure to make an addition for each value of the series.\n",
    "Every value should now be 120, just like the initial number of tasks of the scenario ``scenario120``.\n",
    "As a last example, let us now compute the percentage of tasks closed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Percent Tasks Closed\"] = df[\"closedTasks\"] / df[\"initialOpenTasks\"] * 100\n",
    "\n",
    "### and plot it:\n",
    "plt=df[\"Percent Tasks Closed\"].plot(title=\"Tasks closed %\",figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5721abfe33942d351f7b09905420df100ba8c34b49fc290848df1028bfa2da07"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
